Ee-Peng Lim - IEEE Xplore Author Profile

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Author details

Ee-Peng Lim

Also published under: E. -P. Lim, Ee-peng Lim, E. -L. Lim, Eepeng Lim, Ee–Peng Lim

Publications

85

Citations

1,553

Publications by Year

19982024

Co-Authors:

Palakorn AchananuparpHadeel AlhosainiGary AngXavier Jayaraj Siddarth AshokArindam Banerjee

Show All Co-Authors (146)

Ee-Peng Lim

Also published under: E. -P. Lim, Ee-peng Lim, E. -L. Lim, Eepeng Lim, Ee–Peng Lim

Affiliation

Singapore Management University

Publication Topics

Convolutional Neural Network,
Cross-modal Retrieval,
Food Images,
Generative Adversarial Networks,
Intra-class Variance,
Self-supervised Learning,
Text Encoder,
Textual Information,
Amount Of Time,
Attention Mechanism,
Automatic Method,
Base Classes

Biography

Ee-peng Lim received the Ph.D. degree from the University of Minnesota, Minneapolis, MN, USA. His research expertise include social media mining, social or urban data analytics, and information retrieval. He is currently the Lee Kong Chian Professor with the School of Computing and Information Systems, Singapore Management University, Singapore. He is also the Director of Living Analytics Research Centre in the School, a research centre focusing developing personalized and participatory analytics capabilities for smart city and smart nation relevant applications. He was the recipient of the Distinguished Contribution Award at the 2019 Pacific Asia Conference on Knowledge Discovery and Data Mining, and the Test of Time Award at 2020 ACM Conf...

Publications

85

Citations

1,553

Publications by Year

19982024

Co-Authors:

Palakorn Achananuparp
Hadeel Alhosaini
Gary Ang
Xavier Jayaraj Siddarth Ashok
Arindam Banerjee

Show All Co-Authors (146)

Author's Published Works

Search History

Showing 1-25 of 51 results

Journals (33)

Conferences (16)

Early Access Articles (2)

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Rui Song(51)
Jiaojiao Li(44)
Yunsong Li(42)
Qian Du(26)
Chaoxiong Wu(12)
CAS Key Laboratory of Spectral Imaging Technology, Xi’an, China(18)
Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS, USA(16)
State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an, China(15)
Department of Electronic and Computer Engineering, Mississippi State University, Starkville, MS, USA(9)
State Key Laboratory of Integrated Service Networks, School of Telecommunications Engineering, Xidian University, Xi’an, China(9)
IEEE Transactions on Geoscience and Remote Sensing(20)
IEEE Transactions on Neural Networks and Learning Systems(5)
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS(3)
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)(3)
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing(3)
IEEE(51)
Brussels, Belgium(3)
Vancouver, BC, Canada(3)
New Orleans, LA, USA(2)
Seattle, WA, USA(2)
Waikoloa, HI, USA(2)
Convolutional Neural Network(27)
Spatial Information(18)
Convolutional Layers(14)
RGB Images(14)
Feature Maps(13)

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Results

Contrastive MLP Network Based on Adjacent Coordinates for Cross-Domain Zero-Shot Hyperspectral Image Classification

Jiaojiao Li;Zhiyuan Zhang;Rui Song;Haitao Xu;Yunsong Li;Qian Du

IEEE Transactions on Circuits and Systems for Video Technology

Year: 2025 | Early Access Article |

With the breakthrough of transfer learning and meta-learning, cross-domain few-shot hyperspectral image classification (CDFSL HSIC) technology has recently achieved satisfactory performance under limited annotations. Nevertheless, the most practical applications are zero-shot scenarios, which are intractable for CDFSL technology, such as the extraterrestrial detection scene, where unexplored objec...Show More

Contrastive MLP Network Based on Adjacent Coordinates for Cross-Domain Zero-Shot Hyperspectral Image Classification

Jiaojiao Li;Zhiyuan Zhang;Rui Song;Haitao Xu;Yunsong Li;Qian Du

IEEE Transactions on Circuits and Systems for Video Technology

Year: 2025 | Early Access Article |

Uncertainty-Guided Discriminative Priors Mining for Flexible Unsupervised Spectral Reconstruction

Yihong Leng;Jiaojiao Li;Rui Song;Yunsong Li;Qian Du

IEEE Transactions on Neural Networks and Learning Systems

Year: 2025 | Early Access Article |

Existing supervised spectral reconstruction (SR) methods adopt paired RGB images and hyperspectral images (HSIs) to drive the overall paradigms. Nonetheless, in practice“, paired” requires higher device requirements such as specific well-calibrated dual cameras or more complex and exact registration processes among images with different time phases, widths, and spatial resolution. To tackle the ab...Show More

Uncertainty-Guided Discriminative Priors Mining for Flexible Unsupervised Spectral Reconstruction

Yihong Leng;Jiaojiao Li;Rui Song;Yunsong Li;Qian Du

IEEE Transactions on Neural Networks and Learning Systems

Year: 2025 | Early Access Article |

Background Suppression Network With Attention Collapse Inhibited Transformer for Optical Remote Sensing Object Detection

Jiaojiao Li;Haile Li;Haitao Xu;Rui Song;Yunsong Li;Qian Du

IEEE Transactions on Geoscience and Remote Sensing

Year: 2025 | Volume: 63 | Journal Article |

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Object detection for remote sensing imagery (RSI) has been extensively exploited in practical applications. However, similar and multiscale objects in RSI, especially small objects, pose challenges to RSI object detection methods. Particularly, existing approaches ignore irrelevant background in RSI leading to hardship in discriminative feature extraction, resulting in instances of false positive ...Show More

Background Suppression Network With Attention Collapse Inhibited Transformer for Optical Remote Sensing Object Detection

Jiaojiao Li;Haile Li;Haitao Xu;Rui Song;Yunsong Li;Qian Du

IEEE Transactions on Geoscience and Remote Sensing

Year: 2025 | Volume: 63 | Journal Article |

Progressive Spatial Information-Guided Deep Aggregation Convolutional Network for Hyperspectral Spectral Super-Resolution

Jiaojiao Li;Songcheng Du;Rui Song;Yunsong Li;Qian Du

IEEE Transactions on Neural Networks and Learning Systems

Year: 2025 | Volume: 36, Issue: 1 | Journal Article |

Cited by: Papers (6)

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Fusion-based spectral super-resolution aims to yield a high-resolution hyperspectral image (HR-HSI) by integrating the available high-resolution multispectral image (HR-MSI) with the corresponding low-resolution hyperspectral image (LR-HSI). With the prosperity of deep convolutional neural networks, plentiful fusion methods have made breakthroughs in reconstruction performance promotions. Neverthe...Show More

Progressive Spatial Information-Guided Deep Aggregation Convolutional Network for Hyperspectral Spectral Super-Resolution

Jiaojiao Li;Songcheng Du;Rui Song;Yunsong Li;Qian Du

IEEE Transactions on Neural Networks and Learning Systems

Year: 2025 | Volume: 36, Issue: 1 | Journal Article |

SWFormer: Stochastic Windows Convolutional Transformer for Hybrid Modality Hyperspectral Classification

Jiaojiao Li;Zhiyuan Zhang;Yuzhe Liu;Rui Song;Yunsong Li;Qian Du

IEEE Transactions on Image Processing

Year: 2024 | Volume: 33 | Journal Article |

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Joint classification of hyperspectral images with hybrid modality can significantly enhance interpretation potentials, particularly when elevation information from the LiDAR sensor is integrated for outstanding performance. Recently, the transformer architecture was introduced to the HSI and LiDAR classification task, which has been verified as highly efficient. However, the existing naive transfo...Show More

SWFormer: Stochastic Windows Convolutional Transformer for Hybrid Modality Hyperspectral Classification

Jiaojiao Li;Zhiyuan Zhang;Yuzhe Liu;Rui Song;Yunsong Li;Qian Du

IEEE Transactions on Image Processing

Year: 2024 | Volume: 33 | Journal Article |

Residual Mask in Cascaded Convolutional Transformer for Spectral Reconstruction

Jiaojiao Li;Shiyao Duan;Yihong Leng;Rui Song;Yunsong Li;Qian Du

IEEE Transactions on Geoscience and Remote Sensing

Year: 2024 | Volume: 62 | Journal Article |

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A significant challenge of spectral reconstruction (SR) task is the lower performance reconstructed in foreground regions compared to background regions, which can be attributed to the marked difference in diversity of objects and disparity of adjacent scene characteristics. Moreover, the reconstruction of edge regions is often fraught with substantial errors due to the transitional nature of thes...Show More

Residual Mask in Cascaded Convolutional Transformer for Spectral Reconstruction

Jiaojiao Li;Shiyao Duan;Yihong Leng;Rui Song;Yunsong Li;Qian Du

IEEE Transactions on Geoscience and Remote Sensing

Year: 2024 | Volume: 62 | Journal Article |

PCViT: A Pyramid Convolutional Vision Transformer Detector for Object Detection in Remote-Sensing Imagery

Jiaojiao Li;Penghao Tian;Rui Song;Haitao Xu;Yunsong Li;Qian Du

IEEE Transactions on Geoscience and Remote Sensing

Year: 2024 | Volume: 62 | Journal Article |

Cited by: Papers (10)

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Remote-sensing object detection (RSOD) is a fundamental and valuable task in Earth monitoring. However, remote-sensing images (RSIs) are typically acquired from a bird’s eye perspective, resulting in intrinsic properties such as complex backgrounds, random and dense distribution of objects, and multiscale objects. These properties hinder the direct application of well-performed detection methods i...Show More

PCViT: A Pyramid Convolutional Vision Transformer Detector for Object Detection in Remote-Sensing Imagery

Jiaojiao Li;Penghao Tian;Rui Song;Haitao Xu;Yunsong Li;Qian Du

IEEE Transactions on Geoscience and Remote Sensing

Year: 2024 | Volume: 62 | Journal Article |

HyperMLP: Superpixel Prior and Feature Aggregated Perceptron Networks for Hyperspectral and LiDAR Hybrid Classification

Jiaojiao Li;Yuzhe Liu;Rui Song;Wei Liu;Yunsong Li;Qian Du

IEEE Transactions on Geoscience and Remote Sensing

Year: 2024 | Volume: 62 | Journal Article |

Cited by: Papers (4)

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Hyperspectral images (HSIs) have excellent spectral combining capabilities and light detection and ranging (LiDAR) images have fine stereoscopic elevation information. Therefore, the multimodal fusion classification of hyperspectral and LiDAR images inevitably improves the interpretation ability of remote sensing images. In recent years, the MLP-Mixer, an image processing network based on multilay...Show More

HyperMLP: Superpixel Prior and Feature Aggregated Perceptron Networks for Hyperspectral and LiDAR Hybrid Classification

Jiaojiao Li;Yuzhe Liu;Rui Song;Wei Liu;Yunsong Li;Qian Du

IEEE Transactions on Geoscience and Remote Sensing

Year: 2024 | Volume: 62 | Journal Article |

SCFormer: Spectral Coordinate Transformer for Cross-Domain Few-Shot Hyperspectral Image Classification

Jiaojiao Li;Zhiyuan Zhang;Rui Song;Yunsong Li;Qian Du

IEEE Transactions on Image Processing

Year: 2024 | Volume: 33 | Journal Article |

Cited by: Papers (18)

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Cross-domain (CD) hyperspectral image classification (HSIC) has been significantly boosted by methods employing Few-Shot Learning (FSL) based on CNNs or GCNs. Nevertheless, the majority of current approaches disregard the prior information of spectral coordinates with limited interpretability, leading to inadequate robustness and knowledge transfer. In this paper, we propose an asymmetric encoder-...Show More

SCFormer: Spectral Coordinate Transformer for Cross-Domain Few-Shot Hyperspectral Image Classification

Jiaojiao Li;Zhiyuan Zhang;Rui Song;Yunsong Li;Qian Du

IEEE Transactions on Image Processing

Year: 2024 | Volume: 33 | Journal Article |

Model-Driven Deep Pipeline With Uncertainty-Aware Bundle Adjustment for Satellite Photogrammetry

Kailang Cao;Jiaojiao Li;Rui Song;Zhiqiang Liu;Yunsong Li

IEEE Transactions on Geoscience and Remote Sensing

Year: 2024 | Volume: 62 | Journal Article |

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Bundle adjustment (BA), a vital technology in satellite photogrammetry, directly determines the quality of geographic information mapping. However, the existing BA methods suffer from bottlenecks in the cases of limited stereo views caused by input guidance inadequacy and biased modeling. To conquer these issues, a model-driven satellite photogrammetry deep pipeline (SPDP) is proposed in this arti...Show More

Model-Driven Deep Pipeline With Uncertainty-Aware Bundle Adjustment for Satellite Photogrammetry

Kailang Cao;Jiaojiao Li;Rui Song;Zhiqiang Liu;Yunsong Li

IEEE Transactions on Geoscience and Remote Sensing

Year: 2024 | Volume: 62 | Journal Article |

HPRN: Holistic Prior-Embedded Relation Network for Spectral Super-Resolution

Chaoxiong Wu;Jiaojiao Li;Rui Song;Yunsong Li;Qian Du

IEEE Transactions on Neural Networks and Learning Systems

Year: 2024 | Volume: 35, Issue: 8 | Journal Article |

Cited by: Papers (11)

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Spectral super-resolution (SSR) refers to the hyperspectral image (HSI) recovery from an RGB counterpart. Due to the one-to-many nature of the SSR problem, a single RGB image can be reprojected to many HSIs. The key to tackle this ill-posed problem is to plug into multisource prior information such as the natural spatial context prior of RGB images, deep feature prior, or inherent statistical prio...Show More

HPRN: Holistic Prior-Embedded Relation Network for Spectral Super-Resolution

Chaoxiong Wu;Jiaojiao Li;Rui Song;Yunsong Li;Qian Du

IEEE Transactions on Neural Networks and Learning Systems

Year: 2024 | Volume: 35, Issue: 8 | Journal Article |

Class-Specific Autoaugment Architecture Based on Schmidt Mathematical Theory for Imbalanced Hyperspectral Classification

Jiaojiao Li;Yan Diao;Rui Song;Bobo Xi;Yunsong Li;Qian Du

IEEE Transactions on Geoscience and Remote Sensing

Year: 2023 | Volume: 61 | Journal Article |

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Hyperspectral image classification (HSIC) often suffers from severe imbalanced category distribution in real applications, which causes bias toward the dominated categories. As an effective method, the deep generative model (DGM) can be used to augment the features of imbalanced data through a learnable method to achieve superior classification performance. However, the features extracted by DGM a...Show More

Class-Specific Autoaugment Architecture Based on Schmidt Mathematical Theory for Imbalanced Hyperspectral Classification

Jiaojiao Li;Yan Diao;Rui Song;Bobo Xi;Yunsong Li;Qian Du

IEEE Transactions on Geoscience and Remote Sensing

Year: 2023 | Volume: 61 | Journal Article |

Shape-Constraint Recurrent Flow for 6D Object Pose Estimation

Yang Hai;Rui Song;Jiaojiao Li;Yinlin Hu

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Cited by: Papers (11)

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Most recent 6D object pose methods use 2D optical flow to refine their results. However, the general optical flow methods typically do not consider the target's 3D shape information during matching, making them less effective in 6D object pose estimation. In this work, we propose a shape-constraint recurrent matching framework for 6D object pose estimation. We first compute a pose-induced flow bas...Show More

Shape-Constraint Recurrent Flow for 6D Object Pose Estimation

Yang Hai;Rui Song;Jiaojiao Li;Yinlin Hu

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

FCC: Feature Clusters Compression for Long-Tailed Visual Recognition

Jian Li;Ziyao Meng;Daqian Shi;Rui Song;Xiaolei Diao;Jingwen Wang;Hao Xu

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Cited by: Papers (7)

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Deep Neural Networks (DNNs) are rather restrictive in long-tailed data, since they commonly exhibit an under-representation for minority classes. Various remedies have been proposed to tackle this problem from different perspectives, but they ignore the impact of the density of Backbone Features (BFs) on this issue. Through representation learning, DNNs can map BFs into dense clusters in feature s...Show More

FCC: Feature Clusters Compression for Long-Tailed Visual Recognition

Jian Li;Ziyao Meng;Daqian Shi;Rui Song;Xiaolei Diao;Jingwen Wang;Hao Xu

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Rigidity-Aware Detection for 6D Object Pose Estimation

Yang Hai;Rui Song;Jiaojiao Li;Mathieu Salzmann;Yinlin Hu

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Cited by: Papers (11)

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Most recent 6D object pose estimation methods first use object detection to obtain 2D bounding boxes before actually regressing the pose. However, the general object detection methods they use are ill-suited to handle cluttered scenes, thus producing poor initialization to the subsequent pose network. To address this, we propose a rigidity-aware detection method exploiting the fact that, in 6D pos...Show More

Rigidity-Aware Detection for 6D Object Pose Estimation

Yang Hai;Rui Song;Jiaojiao Li;Mathieu Salzmann;Yinlin Hu

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

MFormer: Taming Masked Transformer for Unsupervised Spectral Reconstruction

Jiaojiao Li;Yihong Leng;Rui Song;Wei Liu;Yunsong Li;Qian Du

IEEE Transactions on Geoscience and Remote Sensing

Year: 2023 | Volume: 61 | Journal Article |

Cited by: Papers (9)

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Spectral reconstruction (SR) aims to recover the hyperspectral images (HSIs) from the corresponding RGB images directly. Most SR studies based on supervised learning require massive data annotations to achieve superior reconstruction performance, which is limited by complicated imaging techniques and laborious annotation calibration in practice. Thus, unsupervised strategies attract the attention ...Show More

MFormer: Taming Masked Transformer for Unsupervised Spectral Reconstruction

Jiaojiao Li;Yihong Leng;Rui Song;Wei Liu;Yunsong Li;Qian Du

IEEE Transactions on Geoscience and Remote Sensing

Year: 2023 | Volume: 61 | Journal Article |

RepCPSI: Coordinate-Preserving Proximity Spectral Interaction Network With Reparameterization for Lightweight Spectral Super-Resolution

Chaoxiong Wu;Jiaojiao Li;Rui Song;Yunsong Li;Qian Du

IEEE Transactions on Geoscience and Remote Sensing

Year: 2023 | Volume: 61 | Journal Article |

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Existing remarkable models for spectral super-resolution (SSR) achieve higher precision at the expense of computations with larger parameters. These algorithms require the heavy memory footprint and sufficient computing power, limiting their practical deployments and applications on portable devices. In this article, we propose an efficient reparameterizing coordinate-preserving proximity spectral...Show More

RepCPSI: Coordinate-Preserving Proximity Spectral Interaction Network With Reparameterization for Lightweight Spectral Super-Resolution

Chaoxiong Wu;Jiaojiao Li;Rui Song;Yunsong Li;Qian Du

IEEE Transactions on Geoscience and Remote Sensing

Year: 2023 | Volume: 61 | Journal Article |

Sal²RN: A Spatial–Spectral Salient Reinforcement Network for Hyperspectral and LiDAR Data Fusion Classification

Jiaojiao Li;Yuzhe Liu;Rui Song;Yunsong Li;Kailiang Han;Qian Du

IEEE Transactions on Geoscience and Remote Sensing

Year: 2023 | Volume: 61 | Journal Article |

Cited by: Papers (21)

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Hyperspectral image (HSI) and light detection and ranging (LiDAR) data fusion have been widely employed in HSI classification to promote interpreting performance. In the existing deep learning methods based on spatial–spectral features, the features extracted from different layers are treated fairly in the learning process. In reality, features extracted from the continuous layers contribute diffe...Show More

Sal²RN: A Spatial–Spectral Salient Reinforcement Network for Hyperspectral and LiDAR Data Fusion Classification

Jiaojiao Li;Yuzhe Liu;Rui Song;Yunsong Li;Kailiang Han;Qian Du

IEEE Transactions on Geoscience and Remote Sensing

Year: 2023 | Volume: 61 | Journal Article |

Structure-Aware Graph Convolution Network for Point Cloud Parsing

Fengda Hao;Jiaojiao Li;Rui Song;Yunsong Li;Kailang Cao

IEEE Transactions on Multimedia

Year: 2023 | Volume: 25 | Journal Article |

Cited by: Papers (4)

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Point clouds are becoming a popular medium to describe 3D scenes, benefitting from their accuracy and completeness in expressing the spatial and geometrical information of objects. However, due to the disorder and uneven distribution nature, merely selecting neighbors for point clouds in Euclidean space is inefficient and position-ignoring. To fill this gap, we propose a structure-aware graph conv...Show More

Structure-Aware Graph Convolution Network for Point Cloud Parsing

Fengda Hao;Jiaojiao Li;Rui Song;Yunsong Li;Kailang Cao

IEEE Transactions on Multimedia

Year: 2023 | Volume: 25 | Journal Article |

Semisupervised Cross-Scale Graph Prototypical Network for Hyperspectral Image Classification

Bobo Xi;Jiaojiao Li;Yunsong Li;Rui Song;Yuchao Xiao;Qian Du;Jocelyn Chanussot

IEEE Transactions on Neural Networks and Learning Systems

Year: 2023 | Volume: 34, Issue: 11 | Journal Article |

Cited by: Papers (51)

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In practice, the acquirement of labeled samples for hyperspectral image (HSI) is time-consuming and labor-intensive. It frequently induces the trouble of model overfitting and performance degradation for the supervised methodologies in HSI classification (HSIC). Fortunately, semisupervised learning can alleviate this deficiency, and graph convolutional network (GCN) is one of the most effective se...Show More

Semisupervised Cross-Scale Graph Prototypical Network for Hyperspectral Image Classification

Bobo Xi;Jiaojiao Li;Yunsong Li;Rui Song;Yuchao Xiao;Qian Du;Jocelyn Chanussot

IEEE Transactions on Neural Networks and Learning Systems

Year: 2023 | Volume: 34, Issue: 11 | Journal Article |

Deep Hybrid 2-D–3-D CNN Based on Dual Second-Order Attention With Camera Spectral Sensitivity Prior for Spectral Super-Resolution

Jiaojiao Li;Chaoxiong Wu;Rui Song;Yunsong Li;Weiying Xie;Lihuo He;Xinbo Gao

IEEE Transactions on Neural Networks and Learning Systems

Year: 2023 | Volume: 34, Issue: 2 | Journal Article |

Cited by: Papers (24)

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A largely ignored fact in spectral super-resolution (SSR) is that the subsistent mapping methods neglect the auxiliary prior of camera spectral sensitivity (CSS) and only pay attention to wider or deeper network framework design while ignoring to excavate the spatial and spectral dependencies among intermediate layers, hence constraining representational capability of convolutional neural networks...Show More

Deep Hybrid 2-D–3-D CNN Based on Dual Second-Order Attention With Camera Spectral Sensitivity Prior for Spectral Super-Resolution

Jiaojiao Li;Chaoxiong Wu;Rui Song;Yunsong Li;Weiying Xie;Lihuo He;Xinbo Gao

IEEE Transactions on Neural Networks and Learning Systems

Year: 2023 | Volume: 34, Issue: 2 | Journal Article |

Feature Guide Network With Context Aggregation Pyramid for Remote Sensing Image Segmentation

Jiaojiao Li;Yuzhe Liu;Jiachao Liu;Rui Song;Wei Liu;Kailiang Han;Qian Du

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Year: 2022 | Volume: 15 | Journal Article |

Cited by: Papers (6)

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In recent years, the deep learning method based on fully convolution networks has proven to be an effective method for the semantic segmentation of remote sensing images (RSIs). However, the rich information and complex content of RSIs make networks training for segmentation more challenging. Specifically, the observing distance between the space-borne cameras and the ground objects is extraordina...Show More

Feature Guide Network With Context Aggregation Pyramid for Remote Sensing Image Segmentation

Jiaojiao Li;Yuzhe Liu;Jiachao Liu;Rui Song;Wei Liu;Kailiang Han;Qian Du

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Year: 2022 | Volume: 15 | Journal Article |

A Triplet Semisupervised Deep Network for Fusion Classification of Hyperspectral and LiDAR Data

Jiaojiao Li;Yinle Ma;Rui Song;Bobo Xi;Danfeng Hong;Qian Du

IEEE Transactions on Geoscience and Remote Sensing

Year: 2022 | Volume: 60 | Journal Article |

Cited by: Papers (19)

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Data fusion of hyperspectral and light detection and ranging (LiDAR) is conducive to obtain more comprehensive surface information and thereby achieve better classification result in Earth monitoring systems. However, lack of labeled samples usually limits the performance of supervised classifiers, and the heterogeneity of multisource data also brings great challenges to data fusion. Aiming to add...Show More

A Triplet Semisupervised Deep Network for Fusion Classification of Hyperspectral and LiDAR Data

Jiaojiao Li;Yinle Ma;Rui Song;Bobo Xi;Danfeng Hong;Qian Du

IEEE Transactions on Geoscience and Remote Sensing

Year: 2022 | Volume: 60 | Journal Article |

Hyperspectral Pansharpening With Adaptive Feature Modulation-Based Detail Injection Network

Yunsong Li;Yuxuan Zheng;Jiaojiao Li;Rui Song;Jocelyn Chanussot

IEEE Transactions on Geoscience and Remote Sensing

Year: 2022 | Volume: 60 | Journal Article |

Cited by: Papers (5)

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Recently, deep learning-based methodologies have attained unprecedented performance in hyperspectral (HS) pansharpening, which aims to improve the spatial quality of HS images (HSIs) by making use of details extracted from the high-resolution panchromatic (HR-PAN) image. However, it remains challenging to incorporate the details into the pansharpened image effectively, while alleviating the spectr...Show More

Hyperspectral Pansharpening With Adaptive Feature Modulation-Based Detail Injection Network

Yunsong Li;Yuxuan Zheng;Jiaojiao Li;Rui Song;Jocelyn Chanussot

IEEE Transactions on Geoscience and Remote Sensing

Year: 2022 | Volume: 60 | Journal Article |

NTIRE 2022 Spectral Recovery Challenge and Data Set

Boaz Arad;Radu Timofte;Rony Yahel;Nimrod Morag;Amir Bernat;Yuanhao Cai;Jing Lin;Zudi Lin;Haoqian Wang;Yulun Zhang;Hanspeter Pfister;Luc Van Gool;Shuai Liu;Yongqiang Li;Chaoyu Feng;Lei Lei;Jiaojiao Li;Songcheng Du;Chaoxiong Wu;Yihong Leng;Rui Song;Mingwei Zhang;Chongxing Song;Shuyi Zhao;Zhiqiang Lang;Wei Wei;Lei Zhang;Renwei Dian;Tianci Shan;Anjing Guo;Chengguo Feng;Jinyang Liu;Mirko Agarla;Simone Bianco;Marco Buzzelli;Luigi Celona;Raimondo Schettini;Jiang He;Yi Xiao;Jiajun Xiao;Qiangqiang Yuan;Jie Li;Liangpei Zhang;Taesung Kwon;Dohoon Ryu;Hyokyoung Bae;Hao-Hsiang Yang;Hua-En Chang;Zhi-Kai Huang;Wei-Ting Chen;Sy-Yen Kuo;Junyu Chen;Haiwei Li;Song Liu;Sabarinathan Sabarinathan;K Uma;B Sathya Bama;S. Mohamed Mansoor Roomi

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2022 | Conference Paper |

Cited by: Papers (52)

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This paper reviews the third biennial challenge on spectral reconstruction from RGB images, i.e., the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB image. This challenge presents the "ARAD_1K" data set: a new, larger-than-ever natural hyperspectral image data set containing 1,000 images. Challenge participants were required to recover hyper-spectral information from s...Show More

NTIRE 2022 Spectral Recovery Challenge and Data Set

Boaz Arad;Radu Timofte;Rony Yahel;Nimrod Morag;Amir Bernat;Yuanhao Cai;Jing Lin;Zudi Lin;Haoqian Wang;Yulun Zhang;Hanspeter Pfister;Luc Van Gool;Shuai Liu;Yongqiang Li;Chaoyu Feng;Lei Lei;Jiaojiao Li;Songcheng Du;Chaoxiong Wu;Yihong Leng;Rui Song;Mingwei Zhang;Chongxing Song;Shuyi Zhao;Zhiqiang Lang;Wei Wei;Lei Zhang;Renwei Dian;Tianci Shan;Anjing Guo;Chengguo Feng;Jinyang Liu;Mirko Agarla;Simone Bianco;Marco Buzzelli;Luigi Celona;Raimondo Schettini;Jiang He;Yi Xiao;Jiajun Xiao;Qiangqiang Yuan;Jie Li;Liangpei Zhang;Taesung Kwon;Dohoon Ryu;Hyokyoung Bae;Hao-Hsiang Yang;Hua-En Chang;Zhi-Kai Huang;Wei-Ting Chen;Sy-Yen Kuo;Junyu Chen;Haiwei Li;Song Liu;Sabarinathan Sabarinathan;K Uma;B Sathya Bama;S. Mohamed Mansoor Roomi

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2022 | Conference Paper |

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